Data-driven fault detection and isolation scheme for a wind turbine benchmark

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dc.creatorIury Valente de Bessa
dc.creatorReinaldo Martinez Palhares
dc.creatorMarcos Flávio Silveira Vasconcelos D'angelo
dc.creatorJoão Edgar Chaves Filho
dc.date.accessioned2025-03-27T13:01:50Z
dc.date.accessioned2025-09-09T00:13:19Z
dc.date.available2025-03-27T13:01:50Z
dc.date.issued2016
dc.identifier.doi10.1016/j.renene.2015.10.061
dc.identifier.issn0960-1481
dc.identifier.urihttps://hdl.handle.net/1843/80987
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofRenewable Energy
dc.rightsAcesso Restrito
dc.subjectTeoria do controle
dc.subjectControle automático
dc.subjectLógica difusa
dc.subject.otherDetecção de Falhas
dc.subject.otherDiagnóstico de Falhas
dc.subject.otherAbordagem Fuzzy/Bayesiana
dc.subject.otherControle
dc.subject.otherControle de Processos
dc.subject.otherInteligência Computacional
dc.subject.otherThe Gibbs sampling is used to indicate the fault event.
dc.subject.otherThe isolation of faults is performed using a Fuzzy/Bayesian network.
dc.subject.otherA wind turbine benchmark is used to illustrate sensor fault isolation.
dc.titleData-driven fault detection and isolation scheme for a wind turbine benchmark
dc.typeArtigo de periódico
local.citation.epage645
local.citation.spage634
local.citation.volume87
local.description.resumoThis paper investigates a new scheme for fault detection and isolation based on time series and data analysis. This scheme is applied in wind turbines which are used to tap the potential of renewable energy. The proposed scheme is performed in two steps and it is based on process data without using any kind of physical modeling. The first step, the fault detection, is based on an alternative method based on the Gibbs sampling algorithm in which the occurrence of a sensor fault is modeled as a change point detection in a time series. The second step, the fault isolation, is handled via a Fuzzy/Bayesian network scheme classifying the kind of fault. The proposed fault detection and isolation (FDI) strategy offers as main contribution the independence from any kind of dynamical modeling and the unprecedented usage of the Gibbs sampling. Furthermore, this work offers a novel data driven FDI approach based on Fuzzy-Bayesian inference and suitable for the wind turbines systems. This approach presents a good performance for detection and diagnostics of sensor faults in a standard wind turbine benchmark.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S0960148115304146

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